CSC412S Spring 2003 - Info
*** LECTURES AND TUTORIALS IN PRATT 266 ***
Course info sheets (ps)(pdf)
Instructor: Sam Roweis; email firstname.lastname@example.org
Tutor: Ruslan Salakhutdinov; email email@example.com
Please do NOT send Roweis or tutor email about the class
directly to their personal accounts.
They are not able to answer class email
except to firstname.lastname@example.org.
Lecture Times: Mondays, Wednesdays 10:10am -- 11:00 am
Lecture Location:Pratt 266
First lecture Jan6, last lecture April 9.
No lectures Feb 17/19 (Reading Week).
Tutorial Times: Fridays, 10:10am-11:00am
Tutorial Location: Pratt 266
First tutorial Jan 10, last tutorial April 11.
No tutorial Feb 21 (Reading Week).
Office Hours: Wednesdays 11-12 or by appointment
Prerequisite: CSC384H, 411H; CGPA 3.0; but permission of
instructor can waive these
Load: 26L, 13T
Michael Jordan, An Introduction to Probabilistic Graphical Models
This textbook is not yet published, but drafts will be provided in class.
2 small assignments worth 10% each
2 larger assignments worth 15% each
1 midterm test worth 25%
1 final test worth 25%
NO FINAL EXAM
A senior undergraduate class on graphical models
and probabilistic networks in AI.
Representing uncertain knowledge using probability and other
formalisms. Qualitative and quantitative specification of probability
distributions using graphical models. Algorithms for inference with
graphical models. Statistical approaches and algorithms for learning
models from experience. Examples will be given of applications of
these models in various areas of artificial intelligence.
Course Information |
Lecture Schedule/Notes |
CSC412 - Uncertainty and Learning in Artificial Intelligence || www.cs.toronto.edu/~roweis/csc412/